1. Technical Field
The present disclosure relates to medical imaging, and more particularly to extracting the myocardium from four-dimensional image data (two-dimensional images over time and space).
2. Discussion of Related Art
Cardiovascular disease is the leading cause of death in the United States. Mortality has been declining over the years as lifestyle has changed, but the decline is also due to the development of new technologies to diagnose disease. One of these techniques is magnetic resonance imaging (MRI), which provides time-varying three-dimensional imagery of the heart. To help in the diagnosis of disease, physicians are interested in identifying heart chambers, the endocardium and epicardium, and measuring changes in ventricular blood volume (ejection fraction) and wall thickening properties over a cardiac cycle. The left ventricle is of particular interest since it pumps oxygenated blood out to distant tissue in the entire body.
There has been a large amount of research on the analysis of medical images. Segmentation of these images has been particularly challenging. In the early nineties, researchers realized that tracking the cardiac wall motion in MR images could be used to characterize meaningful functional changes. A system proposed by S. R. Fleagle, D. R. Thedens, J. C. Ehrhardt, T. D. Scholz, and D. J. Skorton, “Automated identification of left ventricular borders from spin-echo resonance images”, Investigative Radiology, 26:295-303, 1991, delineates the border of the myocardium using a minimum cost path graph search method after a user indicates the center of the left ventricular cavity and an area of interest, for example, with a mouse. D. Geiger, A. Gupta, L. A. Costa, and J. Vlontzos, “Dynamic programming for detecting, tracking, and matching deformable contours”, IEEE Trans. PAMI, 17(3):294-302, 1995, used a dynamic programming approach to refine the contours specified by the user. A. Goshtasby and D. A. Turner, “Segmentation of cardiac cine MR images for extraction of right and left ventricular chambers”, IEEE Trans. Medical Imaging, 14(1):56-64, 1995, proposed a two step method combining intensity thresholding to recover blood from an image and a local gradient to outline strong edges using elastic curves. J. Weng, A. Singh, and M. Y. Chiu, “Learning-based ventricle detection from cardiac MR and CT images”, IEEE Trans. Medical Imaging, 16(4):378-391, 1997, applied a threshold to an image based on parameters estimated during a learning phase to approximate the segmentation.
However, no known system or method exists for providing an adaptive technique of analyzing cardiac images. Therefore, a need exists for a method of cardiac segmentation combining edge, region and shape information in a deformable template.